VisionScribe is a web application that leverages advanced AI models to generate detailed captions and narratives from images. Users can upload an image, and the application will provide a caption describing the contents. The generated captions are detailed and contextually enriched using the BLIP (Bootstrapping Language Image Pretraining) model.

• Image Upload: Users can upload PNG, JPG, or GIF images.
• Automatic Caption Generation: The application generates a basic caption and then enhances it with a detailed narrative using the BLIP model.
• Feedback System: Users can provide feedback by liking or disliking the generated caption and submitting additional comments.
• User Interface: A simple and responsive frontend built with Tailwind CSS and Alpine.js.

• Backend: Python, Flask
• AI Model: BLIP (Salesforce/blip-image-captioning-large)
• Database: SQLite
• Frontend: HTML, Tailwind CSS, Alpine.js
• Image Processing: PIL (Python Imaging Library)
• Deep Learning Framework: PyTorch

• Python 3.8+
• pip (Python package manager)
• torch
• transformers
• Flask
• Pillow
• SQLite (Pre-installed with Python)

git clone https://github.com/wprashed/visionscribe
cd visionscribe

pip install -r requirements.txt

The app uses SQLite to store user feedback. When the app starts, it will automatically create the feedback.db file.

python app.py

Run the following command to start the Flask server:

python app.py

The server will start, and you can access the web app at http://127.0.0.1:5000.

• Navigate to the web interface at http://127.0.0.1:5000.
• Click on the "Click to upload" area or drag and drop an image.
• The app will process the image and generate a detailed caption.
• You can then provide feedback on the caption by either liking or disliking it and submitting additional comments.

After the caption is generated, you will have the option to:

• Like/Dislike: Provide feedback on the generated caption.
• Copy: Copy the caption to your clipboard.
• Submit Feedback: If you liked or disliked the caption, you can provide further comments which will be saved to the database.

The application uses SQLite to store user feedback, which includes:

• id: A unique identifier for each feedback.
• caption: The generated caption.
• liked: Whether the user liked the caption (1 for liked, 0 for disliked).
• comment: The feedback comment from the user.

Uploads an image and returns the generated caption.

Request Body (Form Data):

• file: The image file to be uploaded.

Response:

{
  "caption": "Generated caption text here"
}

Submits user feedback on the caption.

Request Body (JSON):

{
  "caption": "Generated caption text here",
  "liked": 1,
  "comment": "User's comment here"
}

Response:

{
  "success": true
}

Feel free to fork the project, submit issues, and create pull requests. Contributions are welcome!

This project is licensed under the MIT License - see the LICENSE file for details.

• The BLIP model is provided by Salesforce for image captioning and has been integrated into this project.
• Flask is used to build the backend API.
• Tailwind CSS and Alpine.js are used to build the responsive and interactive frontend.